How many repetitions per condition are required for developing a stable growth/no growth boundary model for Bacillus simplex spores?

Modeling the combined effect of temperature, pH, acetic and lactic acid concentrations on the growth/no growth interface of acid-tolerant Bacillus spores

The application of minimal processing technologies has led to increased spoilage incidents in low-acid pasteurized sauces due to the outgrowth of acid-tolerant spore-forming spoilage bacteria (ATSSB). Controlling the germination and subsequent growth of ATSSB spores is vital to enhance the ambient storage stability of pasteurized sauces. This study developed and validated a set of growth/no growth (G/NG) models for spores of two ATSSB strains (Bacillus velezensis and Bacillus subtilis subsp. subtilis) isolated from pasteurized sauces. The G/NG data at two levels of temperature (22 and 30 °C) were collected in Nutrient Broth (a_w = 0.98 adjusted with NaCl) by a full factorial design with five equidistant levels of pH (4.4-5.6), four concentrations of total acetic acid (0.0-0.3% (w/w)), and four concentrations of total lactic acid (0.00-1.00% (w/w)). The growth, starting from heat-treated (10 min 80 °C) spores, of each strain was assessed under 160 combinations by regular optical density measurements during three months. Twelve replicates were made for each combination. The developed models demonstrate that without organic acids even the lowest pH (4.4) allows a high growth possibility of the ATSSB spores, while acetic and lactic acids exhibit a significant antibacterial activity, which can be enhanced at decreased pH. The growth starting from B. subtilis spores can be inhibited for at least three months with 1.0% (w/w) total lactic acid in the water phase at both temperatures, which was not the case for B. velezensis, while 0.3% acetic acid achieves a full inhibition on both strains at 22 °C. With a combination of 0.3% acetic acid and 0.7% lactic acid, no growth should occur in the investigated range. This research is one of the first studies exploring the feasibility of ambient storage for low-acid pasteurized sauces eliminating preservatives such as benzoic and sorbic acids, and proves the synergistic effect of decreased pH and the presence of acetic and lactic acids on inhibiting bacterial growth from ATSSB spores.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning

To predict bacterial population behavior in food, statistical models with specific function form have been applied in the field of predictive microbiology. Modelers need to consider the linear or non-linear relationship between the response and explanatory variables in the statistical modeling approach. In the present study, we focused on machine learning methods to skip definition of primary and secondary structure model. Support vector regression, extremely randomized trees regression, and Gaussian process regression were used to predict population growth of Escherichia coli O157 at 15 and 25 °C without defining the primary and secondary models. Furthermore, the support vector regression model was applied to predict small population of bacteria cells with probability theory. The model performance of the machine learning models were nearly equal to that of the current statistical models. Machine learning models have a potential for predicting bacterial population behavior.